{"id":8836,"date":"2021-01-11T03:20:35","date_gmt":"2021-01-11T08:20:35","guid":{"rendered":"https:\/\/www.iotm2mcouncil.org\/?post_type=news&#038;p=8836"},"modified":"2021-01-11T03:20:37","modified_gmt":"2021-01-11T08:20:37","slug":"mit-smart-thermostat-learns-building-thresholds","status":"publish","type":"news","link":"https:\/\/www.iotm2mcouncil.org\/iot-library\/news\/smart-building-construction-news\/mit-smart-thermostat-learns-building-thresholds\/","title":{"rendered":"MIT smart thermostat learns building thresholds"},"content":{"rendered":"\n<div class=\"wp-block-image\"><figure class=\"alignleft size-large is-resized\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.iotm2mcouncil.org\/wp-content\/uploads\/2021\/01\/mit.jpg\" alt=\"\" class=\"wp-image-8837\" width=\"394\" height=\"319\"\/><\/figure><\/div>\n\n\n\n<p>Researchers at the Massachusetts Institute of Technology (MIT) have designed a smart thermostat that uses data-efficient algorithms to learn optimal temperature thresholds within a week.<\/p>\n\n\n\n<p>Buildings account for about 40 per cent of US energy consumption, and are responsible for one-third of global carbon dioxide emissions. Making buildings more energy efficient is not only a cost-saving measure, but a climate change mitigation strategy. Hence the rise of smart buildings, which are increasingly becoming the norm around the world.<\/p>\n\n\n\n<p>Smart buildings automate systems such as HVAC, lighting, electricity and security. Automation requires sensory data, such as indoor and outdoor temperature and humidity, carbon dioxide concentration, and occupancy status. Smart buildings leverage data in a combination of technologies that can make them more energy efficient.<\/p>\n\n\n\n<p>Since HVAC systems account for nearly half of a building\u2019s energy use, smart buildings use smart thermostats, which automate HVAC controls and can learn the temperature preferences of a building\u2019s occupants.<\/p>\n\n\n\n<p>In a paper in the journal <em>Applied Energy<\/em>, researchers from the MIT Laboratory for Information &amp; Decision Systems (LIDS), in collaboration with Skoltech scientists, have designed a smart thermostat that uses data-efficient algorithms that can learn optimal temperature thresholds within a week.<\/p>\n\n\n\n<p>\u201cDespite recent advances in internet-of-things technology and data analytics, implementation of smart buildings is impeded by the time-consuming process of data acquisition in buildings,\u201d said co-author Munther Dahleh, director of the Institute for Data, Systems &amp; Society (IDSS).<\/p>\n\n\n\n<p>Smart thermostat algorithms use building data to learn how to operate optimally, but the data can take months to collect.<\/p>\n\n\n\n<p>To speed up the learning process, the researchers used a method called manifold learning, where complex and high-dimensional functions are represented by simpler and lower-dimensional functions called manifolds. By leveraging manifold learning and knowledge of building thermodynamics, the researchers replaced a generic control method, which can have many parameters, with a set of threshold policies that each have fewer, more interpretable parameters. Algorithms developed to learn optimal manifolds require fewer data, so they are more data efficient.<\/p>\n\n\n\n<p>The algorithms developed for the thermostat employ a methodology called reinforcement learning (RL), a data-driven sequential decision-making and control approach that has gained much attention in recent years for mastering games such as backgammon and Go.<\/p>\n\n\n\n<p>\u201cWe have efficient simulation engines for computer games that can generate abundant data for the RL algorithms to learn a good playing strategy,\u201d said Ashkan Haji Hosseinloo, a postdoc at LIDS and the lead author of the paper. \u201cHowever, we do not have the luxury of big data for microclimate control in buildings.\u201d<\/p>\n\n\n\n<p>With a background in mechanical engineering and training in methods such as RL, Hosseinloo can apply insights from statistics and computing to real-world physical systems.<\/p>\n\n\n\n<p>\u201cMy main motivation is to slow down, and even prevent, an energy and environmental crisis by improving the efficiency of these systems,\u201d he said.<\/p>\n\n\n\n<p>The smart thermostat\u2019s RL algorithms are event-triggered, meaning they make decisions only when certain events occur, rather than on a predetermined schedule. These events are defined by certain conditions reaching a threshold, such as the temperature in a room dropping out of optimal range.<\/p>\n\n\n\n<p>\u201cThis enables less-frequent learning updates and makes our algorithms computationally less expensive,\u201d Hosseinloo said.<\/p>\n\n\n\n<p>Computational power is a potential constraint for learning algorithms, and computational resources depend on whether algorithms run in the cloud or on a device itself, such as a smart thermostat.<\/p>\n\n\n\n<p>\u201cWe need learning algorithms that are both computationally efficient and data efficient,\u201d said Hosseinloo.<\/p>\n\n\n\n<p>Energy-efficient buildings offer additional advantages beyond reducing emissions and cutting costs. A building\u2019s microclimate and air quality can directly affect the productivity and decision-making performance of building occupants. Considering the many large-scale economic, environmental and societal impacts, microclimate control has become an important issue for governments, building managers and even homeowners.<\/p>\n\n\n\n<p>\u201cThe new generation of smart buildings aims to learn from data how to operate autonomously and with minimum user interventions,\u201d said co-author Henni Ouerdane, a professor at Skoltech, Russia\u2019s Skolkovo Institute of Science &amp; Technology. \u201cA learning thermostat can potentially learn how to adjust its set-point temperatures in coordination with other HVAC devices, or based on its prediction of electricity tariffs in order to save energy and cost.\u201d<\/p>\n\n\n\n<p>Hosseinloo also believes the methodology and algorithms can apply to a diverse range of other physics-based control problems in areas including robotics, autonomous vehicles and transportation, where data and computational efficiency are important.<\/p>\n\n\n\n<p>This research was a Skoltech-MIT joint project conducted as part of the MIT Skoltech Next Generation programme.<\/p>\n","protected":false},"author":26710,"featured_media":8837,"template":"","news-category":[58],"library-tag":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>MIT smart thermostat learns building thresholds - IoT M2M Council<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.iotm2mcouncil.org\/iot-library\/news\/smart-building-construction-news\/mit-smart-thermostat-learns-building-thresholds\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MIT smart thermostat learns building thresholds - IoT M2M Council\" \/>\n<meta property=\"og:description\" content=\"Researchers at the Massachusetts Institute of Technology (MIT) have designed a smart thermostat that uses data-efficient algorithms to learn optimal temperature thresholds within a week. 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